Speaker
Description
Mathematical modelling is the primary tool to predict and control the evolution of complex natural and engineered systems. However, sometimes many different models might predict similar behavior. This might typically happen when the microscopic rules governing an observed phenomenon are not known, and one then has to rely on effective, empirical models.
In this talk, I will introduce an iterative approach leveraging optimal control that aims at selecting the best model to predict the evolution of a reference system. I will illustrate the outcome of the closed-loop algorithm when the reference system is numerically simulated. Then, I will show an implementation of the algorithm on an electrophysiology experiment, where we compared different models of the photocurrent response of cells made sensitive to light by optogenetics.
To conclude the talk, I will move to an open loop framework that aims at quantifying the uncertainty when one controls such experiments assuming a mathematical model.
